解决多模态回归中组件数难确定的问题，自动识别数据分布模式
CEC-MMR: Cross-Entropy Clustering Approach to Multi-Modal Regression
- 基于交叉熵聚类自动确定回归中的分布组件数量
- 在多个数据集上优于传统混合密度网络，预测更准确
- 适合处理具有多峰分布的回归任务，如复杂传感器数据
在实际回归分析中，单个属性常对应多个取值，传统高斯分布因均值位于峰谷之间而表现不佳。为此，通常采用由神经网络学习参数的混合密度网络（MDN），但其关键缺陷在于无法准确确定组件数量。本文提出新型方法 CEC-MMR，基于交叉熵聚类（CEC），可自动检测回归问题中的组件数目，并能为给定属性值唯一匹配到对应成分。实验表明，该方法在多个基准数据集上显著优于经典 MDN，提升了预测精度。
原文摘要 · Abstract (English)
In practical applications of regression analysis, it is not uncommon to encounter a multitude of values for each attribute. In such a situation, the univariate distribution, which is typically Gaussian, is suboptimal because the mean may be situated between modes, resulting in a predicted value that differs significantly from the actual data. Consequently, to address this issue, a mixture distribution with parameters learned by a neural network, known as a Mixture Density Network (MDN), is typically employed. However, this approach has an important inherent limitation, in that it is not feasible to ascertain the precise number of components with a reasonable degree of accuracy. In this paper, we introduce CEC-MMR, a novel approach based on Cross-Entropy Clustering (CEC), which allows for the automatic detection of the number of components in a regression problem. Furthermore, given an attribute and its value, our method is capable of uniquely identifying it with the underlying component. The experimental results demonstrate that CEC-MMR yields superior outcomes compared to classical MDNs.
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